def patches_by_entropy(self, num_patches):
"""
Finds high-entropy patches based on label, allows net to learn borders more effectively.
:param num_patches: int, defaults to num_samples,
enter in quantity it using in conjunction with randomly sampled patches.
:return: list of patches (num_patches, 4, h, w) selected by highest entropy
"""
patches, labels = [], []
ct = 0
while ct < num_patches:
im_path = random.choice(self.train_data)
fn = os.path.basename(im_path)
label = io.imread('Labels/' + fn[:-4] + 'L.png')
# pick again if slice is only background
if len(np.unique(label)) == 1:
continue
img = io.imread(im_path).reshape(5, 240, 240)[:-1].astype('float')
l_ent = entropy(label, disk(self.h))
top_ent = np.percentile(l_ent, 90)
# restart if 80th entropy percentile = 0
if top_ent == 0:
continue
highest = np.argwhere(l_ent >= top_ent)
p_s = random.sample(highest, 3)
for p in p_s:
p_ix = (p[0] - (self.h / 2), p[0] + ((self.h + 1) / 2), p[1] - (self.w / 2),
p[1] + ((self.w + 1) / 2))
patch = np.array([i[p_ix[0]: p_ix[1], p_ix[2]: p_ix[3]] for i in img])
# exclude any patches that are too small
if np.shape(patch) != (4, 65, 65):
continue
patches.append(patch)
labels.append(label[p[0], p[1]])
ct += 1
return np.array(patches[:self.num_samples]), np.array(labels[:self.num_samples])
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